Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm
作者：
Highlights：
• This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm.
• Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis.
• The controller is designed on the sliding manifold selected and the trajectory of the system with this controller are analyzed in detail.
• Based on the continuous sampling, this paper further draws new results with the periodic sampling rule.
• Finally, some numerical examples are given to verify the correctness of the theoretical results.
摘要
•This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm.•Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis.•The controller is designed on the sliding manifold selected and the trajectory of the system with this controller are analyzed in detail.•Based on the continuous sampling, this paper further draws new results with the periodic sampling rule.•Finally, some numerical examples are given to verify the correctness of the theoretical results.
论文关键词：Memristive neural network,Sliding mode control,Event-triggering,Periodic sampling
论文评审过程：Received 20 October 2019, Revised 28 March 2020, Accepted 10 May 2020, Available online 21 May 2020, Version of Record 21 May 2020.
论文官网地址：https://doi.org/10.1016/j.amc.2020.125379
